Integration of Machine Learning Algorithms with Gompertz Curves and Kriging to Estimate Resources in Gold Deposits

Zhang, Steven E.; Nwaila, Glen T.; Tolmay, Leon C.K.; Frimmel, Hartwig E.; Bourdeau, Julie E.

doi:10.1007/s11053-020-09750-z

Cited by 21 publications

(10 citation statements)

References 25 publications

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“…Sensors can also enable in-situ and rapid estimation of resource grades by using trained models that relate in-mine observational and analytical data (e.g., through remote sensing) to resource potential (e.g., grades and uncertainty) (Daniels, 2015;Nwaila et al, 2019;Samson, 2019;Zhang et al, 2021c). Leveraging this type of technology, in principle, it should be possible to perform more accurate and timely grade control and in-situ assessments to drive rapid feedback between the tactical and operational levels.…”

Section: Pre-concentrationmentioning

confidence: 99%

Moving towards deep underground mineral resources: Drivers, challenges and potential solutions

et al. 2023

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Section: Pre-concentrationmentioning

confidence: 99%

Moving towards deep underground mineral resources: Drivers, challenges and potential solutions

et al. 2023

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“…The century-long mining history of Witwatersrand-type ore has produced a legacy of readily available datasets about (1) resource location, (2) resource estimation and (3) resource extraction, all of which can be used for 3D visualization and datadriven analytics (Ghorbani et al, 2020;Nwaila, et al, 2020c;Zhang et al, 2021). These latter published studies have targeted ore deposits hosted by the Central Rand Group of the Witwatersrand Basin.…”

Section: Introductionmentioning

confidence: 99%

Constraints on the Geometry and Gold Distribution in the Black Reef Formation of South Africa Using 3D Reflection Seismic Data and Micro-X-ray Computed Tomography

Nwaila

Manzi²,

Zhang³

et al. 2022

Nat Resour Res

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Geological and geophysical models are essential for developing reliable mine designs and mineral processing flowsheets. For mineral resource assessment, mine planning, and mineral processing, a deeper understanding of the orebody's features, geology, mineralogy, and variability is required. We investigated the gold-bearing Black Reef Formation in the West Rand and Carletonville goldfields of South Africa using approaches that are components of a transitional framework toward fully digitized mining: (1) high-resolution 3D reflection seismic data to model the orebody; (2) petrography to characterize Au and associated ore constituents (e.g., pyrite); and (3) 3D micro-X-ray computed tomography (lCT) and machine learning to determine mineral association and composition. Reflection seismic reveals that the Black Reef Formation is a planar horizon that dips < 10°and has a well-preserved and uneven paleotopography. Several large-scale faults and dikes (most dipping between 65°a nd 90°) crosscut the Black Reef Formation. Petrography reveals that gold is commonly associated with pyrite, implying that lCT can be used to assess gold grades using pyrite as a proxy. Moreover, we demonstrate that machine learning can be used to discriminate between pyrite and gold based on physical characteristics. The approaches in this study are intended to supplement rather than replace traditional methodologies. In this study, we demonstrated that they permit novel integration of micro-scale observations into macroscale modeling, thus permitting better orebody assessment for exploration, resource estimation, mining, and metallurgical purposes. We envision that such integrated approaches will become a key component of future geometallurgical frameworks.

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“…Even though the implementation of artificial intelligence and autonomous technologies in the mining industry began decades ago [26,27], it was not until 1993 that ML applications in mineral resource estimation gained enormous research interest. Zhang et al [28] noted that ML improves resource estimation in the following ways: (i) samples that are rejected in conventional resource estimates because they do not satisfy all quality control requirements can be used provided that the geological descriptions and measurements are reliable; and (ii) resource estimation block models can be constructed using fewer assays and more geology, leading to a reduction in operational costs. Additionally, the ML-based resource estimation approach is significantly cheaper and faster than conventional resource estimation [28].…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [28] noted that ML improves resource estimation in the following ways: (i) samples that are rejected in conventional resource estimates because they do not satisfy all quality control requirements can be used provided that the geological descriptions and measurements are reliable; and (ii) resource estimation block models can be constructed using fewer assays and more geology, leading to a reduction in operational costs. Additionally, the ML-based resource estimation approach is significantly cheaper and faster than conventional resource estimation [28]. In addition, ML can modernize hypothesis-testing and geological modeling, contributing to the understanding of various deposit types estimation [28].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the ML-based resource estimation approach is significantly cheaper and faster than conventional resource estimation [28]. In addition, ML can modernize hypothesis-testing and geological modeling, contributing to the understanding of various deposit types estimation [28]. Moreover, ML techniques can be employed to address operational challenges and improve safety in different sectors of the mining industry, including mineral prospecting and exploration, mineral evaluation, mine planning, mine scheduling, equipment selection, underground and surface equipment operation, drilling and blasting, mineral processing, and mine reclamation [26,[29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning—A Review of Applications in Mineral Resource Estimation

Dumakor-Dupey

Arya

2021

Energies

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Mineral resource estimation involves the determination of the grade and tonnage of a mineral deposit based on its geological characteristics using various estimation methods. Conventional estimation methods, such as geometric and geostatistical techniques, remain the most widely used methods for resource estimation. However, recent advances in computer algorithms have allowed researchers to explore the potential of machine learning techniques in mineral resource estimation. This study presents a comprehensive review of papers that have employed machine learning to estimate mineral resources. The review covers popular machine learning techniques and their implementation and limitations. Papers that performed a comparative analysis of both conventional and machine learning techniques were also considered. The literature shows that the machine learning models can accommodate several geological parameters and effectively approximate complex nonlinear relationships among them, exhibiting superior performance over the conventional techniques.

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Integration of Machine Learning Algorithms with Gompertz Curves and Kriging to Estimate Resources in Gold Deposits

Cited by 21 publications

References 25 publications

Moving towards deep underground mineral resources: Drivers, challenges and potential solutions

Moving towards deep underground mineral resources: Drivers, challenges and potential solutions

Constraints on the Geometry and Gold Distribution in the Black Reef Formation of South Africa Using 3D Reflection Seismic Data and Micro-X-ray Computed Tomography

Machine Learning—A Review of Applications in Mineral Resource Estimation

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